schedule-x VS java

This article will discuss the general concept of building a cross-framework frontend library. It will also display some examples of how this was applied when building the event calendar Schedule-X.

Ah, never even thought of having a kind of overlap tolerance. I might look into if this would make sense for Schedule-X too.

Solving overlapping events was definitely one of the things were a couple of days went into finding a nice and performant solution. My solution is here: https://github.com/schedule-x/schedule-x/blob/main/packages/...

Basically what I do is iterate over a list of events, sorted by start time, and for each:

There are Java bindings for TensorFlow, but that's quite low level. I tried to see if I can get some Keras API for Scala, but I'm no expert and haven't had enough time to invest in this, so it's stuck in alpha. Maybe I develop it slow burning over the next year. A bit envious that Kotlin has a Keras-like library.

Hope this is not too late to answer your question. In theory there are no official Java tutorials for Tensorflow 2. The Java implementation is still under development at https://github.com/tensorflow/java

To give a contrasting perspective, I think the Java ecosystem is much better suited for many data science tasks, and has a growing and well-maintained set of libraries for general purpose machine learning. I won't list them all, but TF-Java, DJL et al. have implementations of many modern architectures and there are a number of excellent libraries (CoreNLP, Lucene et al.) for working with text.

Here's the latest java git repo https://github.com/tensorflow/java